Does the Number of Pharmacies a Patient Frequents Affect Adherence to Statins?
Bibliographic record
Abstract
BACKGROUND: We hypothesized that medication adherence is affected by the number of pharmacies a patient frequents. OBJECTIVES: The objective was to estimate the strength of association between the number of pharmacies a patient frequents and adherence to statins. METHODS: Using administrative data from the Nova Scotia Seniors' Pharmacare program, a retrospective cohort study was conducted among subjects aged 65 years and older first dispensed statin between 1998 and 2008. The Usual Provider of Care (UPC), was defined as the number of dispensation days from the most frequented pharmacy divided by the total number of dispensation days. Estimated adherence of over 80% of the Medication Possession Ratio was defined as adherent. Data were analyzed using hierarchical linear regression. RESULTS: The cohort of 25,641 subjects was 59% female with a mean age of 74 years. During follow-up, subjects filled prescriptions in a median of 2 (mean = 2; standard deviation = 0.88) pharmacies and visited pharmacies a median of 28 (mean = 30) times. During that time, 61% of patients used one pharmacy exclusively. Among subjects using 1 pharmacy, 59% were adherent while 58% using more than one pharmacy were adherent. However, upon adjustment for differences in distributions of age, sex, and other confounders, subjects who used more than one pharmacy had 10% decreased odds of statin adherence (odds ratio: 0.90, 95% confidence interval: 0.86-0.96). These results were robust in sensitivity analyses. CONCLUSIONS: Among seniors newly starting statin therapy, using a single community pharmacy was modestly associated with adherence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".